# Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors

arXiv: https://arxiv.org/abs/2012.06916 · HTML: https://mlsanity.com/paper/2012.06916/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2020.

## Built on by (most cited first, 10 of 10)

- [Statistical process monitoring of artificial neural networks](https://mlsanity.com/paper/2209.07436/) (2022)
- [Simulation-Free Determination of Microstructure Representative Volume Element Size via Fisher Scores](https://mlsanity.com/paper/2404.15207/) (2024)
- [A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images](https://mlsanity.com/paper/2502.07107/) (2025)
- [Bootstrapped Control Limits for Score-Based Concept Drift Control Charts](https://mlsanity.com/paper/2507.16749/) (2025)
- [Mutual Information Surprise: Rethinking Unexpectedness in Autonomous Systems](https://mlsanity.com/paper/2508.17403/) (2025)
- [Monitoring the calibration of probability forecasts with an application to concept drift detection involving image classification](https://mlsanity.com/paper/2510.25573/) (2025)
- [An Adaptive Sampling Framework for Detecting Localized Concept Drift under Label Scarcity](https://mlsanity.com/paper/2511.02452/) (2025)
- [ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning](https://mlsanity.com/paper/2607.08443/) (2026)
- [A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturing](https://mlsanity.com/paper/2607.18164/) (2026)
- [Quickest Change-point Detection in Generalized Linear Models with Variational Estimation](https://mlsanity.com/paper/2610.16616/) (2026)

## Abstract (arXiv)

Supervised learning models are one of the most fundamental classes of models. Viewing supervised learning from a probabilistic perspective, the set of training data to which the model is fitted is usually assumed to follow a stationary distribution. However, this stationarity assumption is often violated in a phenomenon called concept drift, which refers to changes over time in the predictive relationship between covariates X and a response variable Y and can render trained models suboptimal or obsolete. We develop a comprehensive and computationally efficient framework for detecting, monitoring, and diagnosing concept drift. Specifically, we monitor the Fisher score vector, defined as the gradient of the log-likelihood for the fitted model, using a form of multivariate exponentially weighted moving average, which monitors for general changes in the mean of a random vector. In spite of the substantial performance advantages that we demonstrate over popular error-based methods, a score-based approach has not been previously considered for concept drift monitoring. Advantages of the proposed score-based framework include applicability to any parametric model, more powerful detection of changes as shown in theory and experiments, and inherent diagnostic capabilities for helping to identify the nature of the changes.